eval_agent_trace_controlled.log

--- Submit Confidence (step 0) --- confidence=0.74 ready=False score=0.590/0.800 cov=0.567 prec=0.400 empty=0 streak=0

--- LLM Output (step 0) ---
{
  "reasoning": "The current configuration has a low mean coverage of 0.567 and a low precision of 0.400, indicating that the retrieval quality is poor. The top_k value is set to 2, which is likely too low for a corpus of this size, leading to insufficient coverage. Additionally, the embedding model 'general' may not be optimal for a Python-documentation corpus, which could also be contributing to the low coverage. I will increase the top_k to 5 to retrieve more chunks and improve coverage, and I will consider switching the embedding model to one that is more suitable for code or software documentation.",
  "action_type": "adjust_top_k",
  "int_value": 5,
  "float_value": null,
  "model_name": null,
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The current configuration has a low mean coverage of 0.567 and a low precision of 0.400, indicating that the retrieval quality is poor. The top_k value is set to 2, which is likely too low for a corpus of this size, leading to insufficient coverage. Additionally, the embedding model 'general' may not be optimal for a Python-documentation corpus, which could also be contributing to the low coverage. I will increase the top_k to 5 to retrieve more chunks and improve coverage, and I will consider switching the embedding model to one that is more suitable for code or software documentation.\",\"action_type\":\"adjust_top_k\",\"int_value\":5,\"float_value\":null,\"model_name\":null,\"enabled\":null,\"query_id\":null}"
}

--- Submit Confidence (step 1) --- confidence=0.79 ready=False score=0.635/0.800 cov=0.733 prec=0.240 empty=0 streak=0

--- LLM Output (step 1) ---
{
  "reasoning": "The current embedding model is set to 'general', which may not be optimal for a Python-documentation corpus. Switching to a model that is more suited for code or software documentation, such as 'code', could improve retrieval quality and coverage. This is particularly important given the low precision observed in the aggregate metrics and the specific query results.",
  "action_type": "swap_embedding_model",
  "int_value": null,
  "float_value": null,
  "model_name": "code",
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The current embedding model is set to 'general', which may not be optimal for a Python-documentation corpus. Switching to a model that is more suited for code or software documentation, such as 'code', could improve retrieval quality and coverage. This is particularly important given the low precision observed in the aggregate metrics and the specific query results.\",\"action_type\":\"swap_embedding_model\",\"int_value\":null,\"float_value\":null,\"model_name\":\"code\",\"enabled\":null,\"query_id\":null}"
}

--- Submit Confidence (step 2) --- confidence=0.94 ready=False score=0.750/0.800 cov=0.933 prec=0.280 empty=0 streak=0
Override: swap_blocked_by_cooldown delta=0 cooldown=2

--- LLM Output (step 2) ---
{
  "reasoning": "The mean precision is quite low (0.280), indicating that while coverage is high, the relevance of the retrieved chunks is not satisfactory. The scores for the queries are also very close to each other, suggesting that the embedding model might not be effectively distinguishing between relevant and irrelevant chunks. Given that the corpus is Python documentation, switching to a more suitable embedding model could improve precision. The current model 'code' may not be the best fit for this specific domain. I will switch to the 'general' embedding model to see if it improves the precision of the retrievals.",
  "action_type": "swap_embedding_model",
  "int_value": null,
  "float_value": null,
  "model_name": "general",
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The mean precision is quite low (0.280), indicating that while coverage is high, the relevance of the retrieved chunks is not satisfactory. The scores for the queries are also very close to each other, suggesting that the embedding model might not be effectively distinguishing between relevant and irrelevant chunks. Given that the corpus is Python documentation, switching to a more suitable embedding model could improve precision. The current model 'code' may not be the best fit for this specific domain. I will switch to the 'general' embedding model to see if it improves the precision of the retrievals.\",\"action_type\":\"swap_embedding_model\",\"int_value\":null,\"float_value\":null,\"model_name\":\"general\",\"enabled\":null,\"query_id\":null}"
}

--- Submit Confidence (step 3) --- confidence=0.92 ready=False score=0.735/0.800 cov=0.933 prec=0.280 empty=0 streak=0

--- LLM Output (step 3) ---
{
  "reasoning": "The current pipeline shows low precision despite high coverage, indicating that while relevant chunks are being retrieved, they are not the most relevant ones. The scores for the retrieved chunks are very close to each other, suggesting that the embedding model may not be effectively distinguishing between relevant and irrelevant chunks. Given that the embedding model is already set to 'code', which is appropriate for a Python-documentation corpus, the next step is to adjust the similarity threshold to allow for a broader selection of relevant chunks. This should help improve precision without sacrificing coverage.",
  "action_type": "adjust_threshold",
  "int_value": 0,
  "float_value": 0.2,
  "model_name": null,
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The current pipeline shows low precision despite high coverage, indicating that while relevant chunks are being retrieved, they are not the most relevant ones. The scores for the retrieved chunks are very close to each other, suggesting that the embedding model may not be effectively distinguishing between relevant and irrelevant chunks. Given that the embedding model is already set to 'code', which is appropriate for a Python-documentation corpus, the next step is to adjust the similarity threshold to allow for a broader selection of relevant chunks. This should help improve precision without sacrificing coverage.\",\"action_type\":\"adjust_threshold\",\"int_value\":0,\"float_value\":0.2,\"model_name\":null,\"enabled\":null,\"query_id\":null}"
}

--- Submit Confidence (step 4) --- confidence=0.90 ready=False score=0.720/0.800 cov=0.933 prec=0.280 empty=0 streak=0

--- LLM Output (step 4) ---
{
  "reasoning": "The current configuration shows low precision (0.280) despite high coverage (0.933), indicating that while many relevant chunks are retrieved, they are not very relevant. The scores for the retrieved chunks are tightly clustered, suggesting that the embedding model may not be optimal for the Python-documentation corpus. Given that the embedding model is already set to 'code', which is appropriate for code-related documents, the next step is to adjust the similarity threshold to allow for more relevant chunks to be retrieved. Increasing the threshold will help filter out less relevant results and improve precision.",
  "action_type": "adjust_threshold",
  "int_value": 0,
  "float_value": 0.3,
  "model_name": null,
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The current configuration shows low precision (0.280) despite high coverage (0.933), indicating that while many relevant chunks are retrieved, they are not very relevant. The scores for the retrieved chunks are tightly clustered, suggesting that the embedding model may not be optimal for the Python-documentation corpus. Given that the embedding model is already set to 'code', which is appropriate for code-related documents, the next step is to adjust the similarity threshold to allow for more relevant chunks to be retrieved. Increasing the threshold will help filter out less relevant results and improve precision.\",\"action_type\":\"adjust_threshold\",\"int_value\":0,\"float_value\":0.3,\"model_name\":null,\"enabled\":null,\"query_id\":null}"
}

--- Submit Confidence (step 5) --- confidence=0.88 ready=False score=0.705/0.800 cov=0.933 prec=0.280 empty=0 streak=0

--- LLM Output (step 5) ---
{
  "reasoning": "The current pipeline shows low precision (0.280) despite high coverage (0.933), indicating that while relevant chunks are being retrieved, many of them are not relevant. The score distribution for the queries is also compressed, suggesting that the embedding model may not be effectively distinguishing between relevant and non-relevant chunks. Since the embedding model is already set to 'code', which is appropriate for Python documentation, the next step is to adjust the similarity threshold to a higher value to improve precision and reduce noise in the retrievals. This should help filter out less relevant chunks while maintaining good coverage.",
  "action_type": "adjust_threshold",
  "int_value": 0,
  "float_value": 0.4,
  "model_name": null,
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The current pipeline shows low precision (0.280) despite high coverage (0.933), indicating that while relevant chunks are being retrieved, many of them are not relevant. The score distribution for the queries is also compressed, suggesting that the embedding model may not be effectively distinguishing between relevant and non-relevant chunks. Since the embedding model is already set to 'code', which is appropriate for Python documentation, the next step is to adjust the similarity threshold to a higher value to improve precision and reduce noise in the retrievals. This should help filter out less relevant chunks while maintaining good coverage.\",\"action_type\":\"adjust_threshold\",\"int_value\":0,\"float_value\":0.4,\"model_name\":null,\"enabled\":null,\"query_id\":null}"
}

--- Submit Confidence (step 6) --- confidence=0.86 ready=False score=0.690/0.800 cov=0.933 prec=0.280 empty=0 streak=0

--- LLM Output (step 6) ---
{
  "reasoning": "The current configuration shows low precision despite high coverage, indicating that while relevant chunks are being retrieved, they are not ranked well. The score distribution is compressed, suggesting that the embedding model may not be optimal for the Python-documentation corpus. Switching to a more suitable embedding model could improve precision without sacrificing coverage.",
  "action_type": "swap_embedding_model",
  "int_value": null,
  "float_value": null,
  "model_name": "general",
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The current configuration shows low precision despite high coverage, indicating that while relevant chunks are being retrieved, they are not ranked well. The score distribution is compressed, suggesting that the embedding model may not be optimal for the Python-documentation corpus. Switching to a more suitable embedding model could improve precision without sacrificing coverage.\",\"action_type\":\"swap_embedding_model\",\"int_value\":null,\"float_value\":null,\"model_name\":\"general\",\"enabled\":null,\"query_id\":null}"
}

--- Submit Confidence (step 7) --- confidence=0.68 ready=False score=0.545/0.800 cov=0.733 prec=0.240 empty=0 streak=0
Override: swap_blocked_by_cooldown delta=0 cooldown=2

--- LLM Output (step 7) ---
{
  "reasoning": "The current pipeline is experiencing low precision (0.240) despite having a mean coverage of 0.733, which indicates that while some relevant chunks are being retrieved, many of them are not relevant. The query Q23 has a coverage of 0.000, suggesting that the retrieval is failing for this query. The scores for Q23 are also quite close together (min=0.477, max=0.507), indicating that the embedding model may not be well-suited for the software domain. Since the embedding model 'code' was previously tested and improved the performance, I will switch back to it. This should help improve the precision and coverage for software-related queries.",
  "action_type": "swap_embedding_model",
  "int_value": null,
  "float_value": null,
  "model_name": "code",
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The current pipeline is experiencing low precision (0.240) despite having a mean coverage of 0.733, which indicates that while some relevant chunks are being retrieved, many of them are not relevant. The query Q23 has a coverage of 0.000, suggesting that the retrieval is failing for this query. The scores for Q23 are also quite close together (min=0.477, max=0.507), indicating that the embedding model may not be well-suited for the software domain. Since the embedding model 'code' was previously tested and improved the performance, I will switch back to it. This should help improve the precision and coverage for software-related queries.\",\"action_type\":\"swap_embedding_model\",\"int_value\":null,\"float_value\":null,\"model_name\":\"code\",\"enabled\":null,\"query_id\":null}"
}

--- Submit Confidence (step 8) --- confidence=0.66 ready=False score=0.530/0.800 cov=0.733 prec=0.240 empty=0 streak=0

--- LLM Output (step 8) ---
{
  "reasoning": "The current configuration shows low precision (0.240) despite a mean coverage of 0.733, indicating that while some relevant chunks are retrieved, they are not precise. The query Q23 has a coverage of 0.000, suggesting that the threshold may still be too high or the top_k is not sufficient to capture relevant chunks. Given that the embedding model has been switched to 'code', which is appropriate for Python documentation, I will increase the top_k to allow for more retrieved chunks, which may help improve coverage and precision.",
  "action_type": "adjust_top_k",
  "int_value": 10,
  "float_value": null,
  "model_name": null,
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The current configuration shows low precision (0.240) despite a mean coverage of 0.733, indicating that while some relevant chunks are retrieved, they are not precise. The query Q23 has a coverage of 0.000, suggesting that the threshold may still be too high or the top_k is not sufficient to capture relevant chunks. Given that the embedding model has been switched to 'code', which is appropriate for Python documentation, I will increase the top_k to allow for more retrieved chunks, which may help improve coverage and precision.\",\"action_type\":\"adjust_top_k\",\"int_value\":10,\"float_value\":null,\"model_name\":null,\"enabled\":null,\"query_id\":null}"
}

--- Submit Confidence (step 9) --- confidence=0.61 ready=False score=0.485/0.800 cov=0.733 prec=0.120 empty=0 streak=0

--- LLM Output (step 9) ---
{
  "reasoning": "The current configuration has a low mean precision (0.120) despite a reasonable mean coverage (0.733). This suggests that while some relevant chunks are being retrieved, they are not of high quality. The scores for Q23 indicate that it is retrieving chunks with scores that are too close together, which may imply that the embedding model is not well-suited for the domain or that the chunk size is too large. Since the embedding model has already been switched to 'code', I will adjust the chunk size to a smaller value to improve the score distribution and retrieval quality.",
  "action_type": "adjust_chunk_size",
  "int_value": 256,
  "float_value": null,
  "model_name": null,
  "enabled": null,
  "query_id": null,
  "raw_content": "{\"reasoning\":\"The current configuration has a low mean precision (0.120) despite a reasonable mean coverage (0.733). This suggests that while some relevant chunks are being retrieved, they are not of high quality. The scores for Q23 indicate that it is retrieving chunks with scores that are too close together, which may imply that the embedding model is not well-suited for the domain or that the chunk size is too large. Since the embedding model has already been switched to 'code', I will adjust the chunk size to a smaller value to improve the score distribution and retrieval quality.\",\"action_type\":\"adjust_chunk_size\",\"int_value\":256,\"float_value\":null,\"model_name\":null,\"enabled\":null,\"query_id\":null}"
}